OHI 2016: Summary of results

This document describes results from the OHI 2016 global assessment.

Scores are broken down as follows:

1. Description of reference datasets and figures
2. The global picture
3. A closer look at regions
4. Change over time: five year trends in scores
5. Comparison of assessment years (what’s changed do due to changes to models and/or data sources)
6. A closer look at goals
7. Additional checks

Description of reference datasets and figures

The following datasets and figures include the complete data for the OHI 2016 assessment.

Datasets (compiled for website)

Data files to generate content for the Ocean Health Index website.

Output files:

• radical_date.csv. This file is also used to generate the tables and figures in this document.
  • File includes the EEZ (updated in 2012-2016), Antarctica (most recently calculated in 2015), and High Seas (most recently calculated in 2014) data. However, years 2012 and 2013 do not have Antarctica or High Seas data. And, the same High Seas and Antarctica data are used for years 2014, 2015, and 2016.
  • Region 0 is the global average scores, which are calculated using an area-weighted average of the scores (status, likely future state, and score dimensions) for EEZ, Antarctica, and High Seas regions.
  • Region 300 is the eez average scores, which are calculated using an area-weighted average of the scores (status, likely future state, and score dimensions) for the EEZ regions.
• DataExplorer_eez2016_date.csv. This file includes only the 2016 data, and is used in the Data Explorer.

Figures that provide an overview of scores

This carpet plot figure (download as high resolution png) provides a full overview of the scores from the 2016 assessment. Each row represents a region, the main groupings represent goals, and within each goal there are 5 years of data. Black regions indicate no data.

Don’t try too hard to interpret the results for specific countries/goals/years!!

This plot is good for providing a quick overview of things like:

• What is the range of scores?
• Which goals tend to have high scores across most regions (species, habitat)
• Which goals have a lot of variation across regions (tourism & recreation, lasting special places)
• Which goals are volatile across years (natural products, tourism & recreation)

Another resource that can be useful for examining scores is this interactive plot. This can be used to (some example screen shots):

explore the distribution of scores

compare different goal scores

observe change over time

More figure resources

• The location of maps describing goal and index scores can be downloaded from here
• Flower plots for each region can be downloaded from here

The global picture

This section describes global patterns in index and goal scores. If you are more interested in what is happening at the region scale, skip to the next section.

Average global goal and index scores across years

(NOTE: Livelihood and economy goals are not included here)

Overall, there weren’t dramatic changes across years.

The Index score for 2016 (eez area weighted average of region scores) was: 71. This value was essentially constant from 2012 to 2016.

I haven’t done a formal analysis, but it looks like over time:

• Clean water scores may be slightly decreasing
• Lasting special places may be slightly increasing
• Natural products decreases rather dramatically

goal	2012	2013	2014	2015	2016
Index	71.1	71.5	71.0	71.0	70.6
Artisanal opportunities	76.6	76.9	77.2	77.2	77.2
Species condition (subgoal)	91.1	91.1	91.1	91.2	91.2
Biodiversity	90.8	90.8	90.8	90.9	90.9
Habitat (subgoal)	90.4	90.5	90.5	90.5	90.6
Coastal protection	87.8	87.7	87.7	87.6	87.3
Carbon storage	79.2	79.2	79.2	79.2	79.3
Clean water	74.2	73.7	73.6	73.5	73.5
Fisheries (subgoal)	53.2	53.5	53.5	52.9	53.4
Food provisioning	53.3	53.6	53.7	52.9	53.2
Mariculture (subgoal)	30.1	32.8	33.7	32.8	31.9
Iconic species (subgoal)	66.2	67.4	67.4	67.8	66.5
Sense of place	61.1	62.6	62.6	63.1	62.4
Lasting special places (subgoal)	56.1	57.7	57.8	58.5	58.4
Natural products	59.0	58.0	56.5	53.2	48.0
Tourism & recreation	49.9	48.7	45.3	48.1	49.2

A closer look at the regions

This section explores goal and index scores at the region level. I mostly focus on the 2016 scores.

Regional goal and index scores for 2016

This interactive table describes the index and goal scores for the regions in 2016 (and here’s a link to a color coded table, and a csv file can also be downloaded).

Distribution of scores

The median index score was 68. The highest score was 91 for Howland Island and Baker Island, and the lowest score was 44 for Sierra Leone.

The following histogram describes the distribution of overall index scores:

The regions with index scores of 80 or greater are:

country	Index
Howland Island and Baker Island	91
Jarvis Island	89
South Georgia and the South Sandwich Islands	88
Christmas Island	85
Seychelles	85
Palmyra Atoll	85
Germany	85
Northern Saint-Martin	84
Cocos Islands	83
Phoenix Islands (Kiribati)	83
New Caledonia	82
Crozet Islands	82
Kerguelen Islands	82
Heard and McDonald Islands	82
Norfolk Island	81
Macquarie Island	81
Antigua and Barbuda	81
American Samoa	81
Aruba	81
Australia	80
Glorioso Islands	80

The regions with index scores of 50 or less are:

country	Index
Eritrea	50
Senegal	50
Lebanon	50
Algeria	50
Republique du Congo	49
Guinea Bissau	49
Liberia	48
Nicaragua	47
Democratic Republic of the Congo	47
Guinea	45
Ivory Coast	45
Libya	44
Sierra Leone	44

Map of index scores

Map png files can be downloaded here.

(Maps of goal scores are described below)

Change over time: five year trends in scores

A color-coded table of 5 year trends is available here (and a csv file).

These values are calculated using a linear model of scores for each region/goal over the past 5 years. Positive values indicate potentially increasing scores during the past 5 years and negative values indicate potentially decreasing scores.

NOTE: Currently, these data include the livelihoods and economies goal but this trends should probably be calculated without this goal.

Trends of regional index scores

The global average suggests a slight (but, probably negligible) decrease in index scores during the past five years (-0.11), but there was a good deal of variation among regions. The following histogram describes the distribution of trends in index scores:

The 10 regions with the largest increases in index scores are:

country	trend
Mozambique	2.46
Samoa	2.32
Prince Edward Islands	2.29
South Georgia and the South Sandwich Islands	2.22
Tokelau	2.19
Solomon Islands	2.05
Tuvalu	2.04
Glorioso Islands	2.04
Tonga	1.82
Maldives	1.80

The 10 regions with the largest decreases in index scores are:

	country	trend
211	Saba	-2.42
212	Ukraine	-2.46
213	Sint Eustatius	-2.48
214	Saudi Arabia	-2.56
215	Norway	-2.56
216	Sint Maarten	-2.59
217	Finland	-2.63
218	Equatorial Guinea	-2.98
219	Estonia	-3.07
220	Eritrea	-4.44

Trends of goal scores

Here is a summary of the mean and standard deviation of the trends for different goals:

goal	mean trend	sd trend
Index	-0.1320909	1.0972232
Artisanal opportunities	0.1703636	0.4172632
Species condition (subgoal)	0.0108182	0.0832187
Biodiversity	-0.0203182	0.3694663
Habitat (subgoal)	-0.0510138	0.7398197
Coastal protection	-0.3630588	2.3081069
Carbon storage	-0.0187838	0.0884785
Clean water	-0.1435000	1.1309845
Economies	1.0139216	2.3537731
Livelihoods & economies	0.4516667	1.7634165
Livelihoods	-0.1106863	2.0470061
Fisheries (subgoal)	0.5097727	2.1771129
Food provisioning	0.4900909	2.1767134
Mariculture (subgoal)	0.0564000	1.9816608
Iconic species (subgoal)	0.1383182	0.4453508
Sense of place	0.4508182	2.0043123
Lasting special places (subgoal)	0.7630000	3.9437809
Natural products	-3.7691406	9.7389830
Tourism & recreation	-0.3442647	3.7713096

These scores require a closer look, but possible patterns include:

• Scores for the artisanal opportunities goal appear to have increased by relatively small amounts for the majority of regions
• Scores for the fisheries goal appear to be increasing for the majority of regions
• Scores for the natural products appear to be overall decreasing, but there is a lot of variation, and some regions have had large increases
• Tourism trends vary strongly from region to region
• Lasting special places trends are fairly variable due to large increases in scores for a few regions

Comparing assessment years

The following is a comparison of the global status scores generated for 2015 by this year’s assessment vs. last year’s assessment.

If the models and source data remains the same, these scores should be exactly the same. Differences indicate changes in methods or source data (described in this downloadable document).

These changes do not reflect changes in actual system health!

Global averages

The following goals had the largest changes:

• Artisanal opportunity (biggest change: +11 points): Reference point of need data adjusted to avoid scores being driven by outliers
• Species condition/biodiversity (+8 points): Addition of bird species tended to increase status scores
• Mariculture (+6 points): Changes to source data
• Fisheries (-4 points): Decrease is due primarily to increase in number of stocks classified as “unidentified” in source data (plus, a lot of other changes)

The rest of the goals/subgoals changed by less than 3 points (on average, although regions might be highly variable).

goal	assess2015	assess2016	change
Artisanal opportunities	62.92	73.53	10.61
Species condition (subgoal)	85.18	92.78	7.60
Biodiversity	86.52	90.35	3.83
Habitat (subgoal)	87.90	87.91	0.01
Coastal protection	85.64	85.53	-0.11
Carbon storage	78.42	78.42	0.00
Clean water	73.47	73.47	0.00
Economies	87.65	87.65	0.00
Livelihoods & economies	82.46	82.46	0.00
Livelihoods	77.28	77.28	0.00
Fisheries (subgoal)	54.91	50.48	-4.43
Food provisioning	54.25	50.50	-3.75
Mariculture (subgoal)	25.44	31.53	6.09
Iconic species (subgoal)	59.27	63.11	3.84
Sense of place	58.66	59.76	1.10
Lasting special places (subgoal)	58.54	56.41	-2.13
Natural products	48.71	50.91	2.20
Tourism & recreation	48.29	46.98	-1.31

Regional data

This color-coded table compares the 2015 index scores generated for each region/goal between this year’s and last year’s assessment.

Because these are index scores, changes reflect updates to pressure and resilience scores as well as status.

The following interactive plot provides an overview of how the 2015 scores changed between the 2015 and 2016 assessment for all goals and dimensions.

Some general pattern in these data:

• Fisheries/Food provisioning: Lots of changes all over the place due to changes in source data and other changes to methods
• Artisanal opportunity: consistently increased for most regions due to change in reference point

A closer look at goals

This section takes a closer look at each goal/subgoal. I do not include goals that are comprised of multiple subgoals, although the subgoals are described.

Artisanal opportunities

Scores

Top 10 performers

country	score
Cayman Islands	100
United Arab Emirates	100
Qatar	100
Bermuda	100
United States	100
Kuwait	100
Norway	100
Ireland	100
Saudi Arabia	100
Brunei	100

Bottom 10 performers

	country	score
211	Ivory Coast	46
212	Solomon Islands	45
213	Cameroon	45
214	Mozambique	44
215	Comoro Islands	44
216	Madagascar	44
217	Benin	44
218	Togo	43
219	Guinea	43
220	Liberia	42

Species condition

Scores

Top 10 performers

country	score
Northern Saint-Martin	98
Aruba	98
Curacao	98
Cayman Islands	98
Ascension	98
Bahamas	98
Saba	98
Montserrat	98
Bonaire	98
Turks and Caicos Islands	98

Bottom 10 performers

	country	score
211	Vietnam	82
212	Singapore	82
213	Eritrea	82
214	Oecussi Ambeno	81
215	Cambodia	81
216	East Timor	81
217	Libya	81
218	Myanmar	80
219	Sudan	80
220	Iraq	78

Habitat

Scores

Top 10 performers

country	score
Kerguelen Islands	100
Heard and McDonald Islands	100
Norfolk Island	100
Macquarie Island	100
Tuvalu	100
Pitcairn	100
Wallis and Futuna	100
British Indian Ocean Territory	100
Suriname	100
Russia	100

Bottom 10 performers

	country	score
208	Colombia	66
209	Nigeria	66
210	Democratic Republic of the Congo	64
211	Belize	62
212	Jan Mayen	61
213	Senegal	61
214	Poland	60
215	Dominica	60
216	Sierra Leone	59
217	Iceland	52

Coastal protection

Scores

Top 10 performers

country	score
Howland Island and Baker Island	100
Phoenix Islands (Kiribati)	100
Aruba	100
Curacao	100
Tuvalu	100
Pitcairn	100
French Polynesia	100
Wallis and Futuna	100
Netherlands	100
British Indian Ocean Territory	100

Bottom 10 performers

	country	score
161	Ivory Coast	33
162	Guinea	31
163	Sierra Leone	31
164	Senegal	30
165	Guinea Bissau	30
166	Nicaragua	30
167	Democratic Republic of the Congo	30
168	Lithuania	29
169	Dominica	27
170	Belize	24

Carbon storage

Scores

Top 10 performers

country	score
Seychelles	100
Germany	100
Northern Saint-Martin	100
Antigua and Barbuda	100
Aruba	100
Netherlands	100
Bahamas	100
Suriname	100
Russia	100
South Africa	100

Bottom 10 performers

	country	score
139	Senegal	34
140	Liberia	34
141	Guinea	33
142	Ivory Coast	33
143	Sierra Leone	33
144	Guinea Bissau	31
145	Democratic Republic of the Congo	30
146	Barbados	27
147	Dominica	27
148	Nicaragua	10

Clean waters

Scores

Top 10 performers

country	score
Heard and McDonald Islands	100
South Georgia and the South Sandwich Islands	99
Kerguelen Islands	99
Falkland Islands	99
Bouvet Island	99
Jarvis Island	98
Macquarie Island	98
Howland Island and Baker Island	97
Phoenix Islands (Kiribati)	97
Crozet Islands	97

Bottom 10 performers

	country	score
211	Israel	33
212	Guatemala	33
213	Belgium	32
214	India	29
215	Benin	29
216	Lebanon	29
217	Slovenia	28
218	Togo	28
219	Monaco	24
220	Gibraltar	20

Fisheries

Scores

Top 10 performers

country	score
Tuvalu	96
Nauru	96
Palau	95
Mayotte	91
Oecussi Ambeno	91
Phoenix Islands (Kiribati)	90
Finland	90
Maldives	89
Seychelles	88
Solomon Islands	88

Bottom 10 performers

	country	score
211	El Salvador	19
212	Amsterdam Island and Saint Paul Island	18
213	Mauritania	17
214	Barbados	17
215	Western Sahara	16
216	Guinea	16
217	Wake Island	12
218	Turks and Caicos Islands	9
219	Jan Mayen	6
220	Bouvet Island	3

Mariculture

Scores

Top 10 performers

country	score
Russia	100
Ecuador	100
New Zealand	100
Norway	100
Chile	100
China	100
Faeroe Islands	100
Iceland	99
Belize	94
Canada	72

Bottom 10 performers

	country	score
116	Kenya	0
117	Jamaica	0
118	Nigeria	0
119	El Salvador	0
120	Pakistan	0
121	Eritrea	0
122	Senegal	0
123	Lebanon	0
124	Algeria	0
125	Libya	0

Iconic species

Scores

Top 10 performers

country	score
Cayman Islands	100
United Arab Emirates	100
Qatar	100
Bermuda	100
United States	100
Kuwait	100
Norway	100
Ireland	100
Saudi Arabia	100
Brunei	100

Bottom 10 performers

	country	score
211	Ivory Coast	46
212	Solomon Islands	45
213	Cameroon	45
214	Mozambique	44
215	Comoro Islands	44
216	Madagascar	44
217	Benin	44
218	Togo	43
219	Guinea	43
220	Liberia	42

Lasting special places

Scores

Top 10 performers

country	score
Howland Island and Baker Island	100
Jarvis Island	100
South Georgia and the South Sandwich Islands	100
Palmyra Atoll	100
Germany	100
Northern Saint-Martin	100
Phoenix Islands (Kiribati)	100
Crozet Islands	100
Kerguelen Islands	100
Heard and McDonald Islands	100

Bottom 10 performers

	country	score
211	Bouvet Island	0
212	Benin	0
213	Iraq	0
214	Syria	0
215	Sudan	0
216	Somalia	0
217	North Korea	0
218	Eritrea	0
219	Liberia	0
220	Libya	0

Natural products

Scores

Top 10 performers

country	score
New Caledonia	100
Suriname	100
Mozambique	100
India	99
Iran	98
French Polynesia	97
Trinidad and Tobago	97
Germany	96
Italy	96
Latvia	96

Bottom 10 performers

	country	score
119	Honduras	0
120	Brunei	0
121	Faeroe Islands	0
122	Cyprus	0
123	Sao Tome and Principe	0
124	Montenegro	0
125	Equatorial Guinea	0
126	Dominica	0
127	Algeria	0
128	Republique du Congo	0

Tourism and recreation

Scores

Top 10 performers

country	score
Seychelles	100
Northern Saint-Martin	100
Antigua and Barbuda	100
Aruba	100
Malta	100
Curacao	100
Vanuatu	100
Maldives	100
Bahamas	100
Saba	100

Bottom 10 performers

	country	score
195	Democratic Republic of the Congo	2
196	Yemen	0
197	Ukraine	0
198	Iraq	0
199	Turkey	0
200	Syria	0
201	Somalia	0
202	North Korea	0
203	Lebanon	0
204	Libya	0

Additional checks

This table describes the years of data that were used for each goal for each assessment year.

Years used in analysis

goals	eez_2012	eez_2013	eez_2014	eez_2015	eez_2016
AO	2011	2012	2013	2014	2015
SPP	NA	NA	NA	NA	NA
BD	NA	NA	NA	NA	NA
HAB	NA	NA	NA	NA	NA
CP	NA	NA	NA	NA	NA
CS	NA	NA	NA	NA	NA
CW	NA	NA	NA	NA	NA
ECO	20122000	20122000	20122000	20122000	20122000
LE	NA	NA	NA	NA	NA
LIV	20102000	20112000	20122000	20122000	20122000
FIS	2006	2007	2008	2009	2010
FP	NA	NA	NA	NA	NA
MAR	2010	2011	2012	2013	2014
ICO	2012	2013	2014	2015	2016
SP	NA	NA	NA	NA	NA
LSP	2011	2012	2013	2014	2015
NP	2009	2010	2011	2012	2013
TR	2010	2011	2012	2013	2014

Analyses of data

One thing we have been interested in is how well the likely future state score actually predicts the future score.

My initial conclusions are that the trend/pressure/resilience components of the likely future state score are not improving our predictions.

Observed vs. predicted 2016 scores

Methods: I compared the likely future status scores in 2012 to the observed status in 2016.

Results: At first glance, this appears promising because there is actaully a nice correlation between the values:

And, the slope estimate isn’t too far from 1 (0.76) and the R2 value is fairly high (0.75):

## 
## Call:
## lm(formula = status_2016 ~ likely_future_state_2012, data = data_sp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.6357  -2.8018   0.1621   2.8615  11.0528 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              21.00566    2.01076   10.45   <2e-16 ***
## likely_future_state_2012  0.65202    0.02876   22.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.509 on 218 degrees of freedom
## Multiple R-squared:  0.7022, Adjusted R-squared:  0.7009 
## F-statistic: 514.1 on 1 and 218 DF,  p-value: < 2.2e-16

Scores 2012 vs. scores 2016

Methods: I compared the 2012 and 2016 status scores to get a feel for how well the 2012 scores predicted the 2016 scores.

Results: The 2012 scores alone do a better job predicting 2016 scores than incorporating the trend/pressure/resilience data. The additional information seems to, overall, make our predictions worse:

## 
## Call:
## lm(formula = status_2016 ~ status_2012, data = data_sp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.9300  -1.4938   0.2677   1.7784  12.1589 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.01133    2.09737   2.389   0.0177 *  
## status_2012  0.92162    0.03143  29.323   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.716 on 218 degrees of freedom
## Multiple R-squared:  0.7977, Adjusted R-squared:  0.7968 
## F-statistic: 859.8 on 1 and 218 DF,  p-value: < 2.2e-16

Predicted change in score vs. observed change in score (from 2012 to 2016)

Methods: Another way to look at these ata is to compare the predicted and observed changes in status from 2012 to 2016.

The predicted change in status was calculated as: status (2012) minus likely future score (2012). The observed change in score was calcualted as status (2012) minus status (2016).

Results: There was no correlation between the predicted change in status and the observed change in status:

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_sp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.9672  -1.5766   0.2428   2.0001  11.2743 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33757    0.29703  -1.136    0.257
## pred_change  0.05436    0.05405   1.006    0.316
## 
## Residual standard error: 3.76 on 218 degrees of freedom
## Multiple R-squared:  0.004617,   Adjusted R-squared:  5.151e-05 
## F-statistic: 1.011 on 1 and 218 DF,  p-value: 0.3157

Looking more closely at patterns within goals/subgoals

The next step in the above analysis is to look within each goal/subgoal to get a better feel for possible relationships between trend and pressure/resilience components.

AO: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.4336  -0.4367  -0.1202   0.5684   4.0700 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.43477    0.16011   2.715  0.00715 **
## pred_change  0.01732    0.01844   0.940  0.34850   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.308 on 218 degrees of freedom
## Multiple R-squared:  0.004033,   Adjusted R-squared:  -0.0005358 
## F-statistic: 0.8827 on 1 and 218 DF,  p-value: 0.3485
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6640 -0.4712 -0.1182  0.5061  4.6456 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 0.330908   0.183614   1.802  0.07290 . 
## trend_2012  8.510248   3.056197   2.785  0.00583 **
## r_minus_p   0.006277   0.004306   1.458  0.14634   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.288 on 217 degrees of freedom
## Multiple R-squared:  0.03917,    Adjusted R-squared:  0.03031 
## F-statistic: 4.423 on 2 and 217 DF,  p-value: 0.0131
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6554 -0.4050 -0.0943  0.4460  4.7771 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -1.73704    1.10443  -1.573  0.11723   
## trend_2012       8.11897    3.04500   2.666  0.00825 **
## pressures_2012   0.01166    0.01037   1.124  0.26218   
## resilience_2012  0.02780    0.01212   2.294  0.02274 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.28 on 216 degrees of freedom
## Multiple R-squared:  0.05494,    Adjusted R-squared:  0.04181 
## F-statistic: 4.186 on 3 and 216 DF,  p-value: 0.006625

BD/SPP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## pred_change        0          0      NA       NA
## 
## Residual standard error: 0 on 218 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 218 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## trend_2012         0          0      NA       NA
## r_minus_p          0          0      NA       NA
## 
## Residual standard error: 0 on 217 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 2 and 217 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0          0      NA       NA
## trend_2012             0          0      NA       NA
## pressures_2012         0          0      NA       NA
## resilience_2012        0          0      NA       NA
## 
## Residual standard error: 0 on 216 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 3 and 216 DF,  p-value: NA

BD/HAB: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.0323  -0.1645   0.1110   0.3672  15.0375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.37627    0.18689  -2.013  0.04533 * 
## pred_change  0.06486    0.02396   2.708  0.00732 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 215 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03297,    Adjusted R-squared:  0.02848 
## F-statistic: 7.331 on 1 and 215 DF,  p-value: 0.007323
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.8677  -0.0233   0.0844   0.1844  15.7936 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.206043   0.297167  -0.693    0.489
## trend_2012   1.496213   1.148257   1.303    0.194
## r_minus_p    0.004269   0.007678   0.556    0.579
## 
## Residual standard error: 2.188 on 212 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.009962,   Adjusted R-squared:  0.0006217 
## F-statistic: 1.067 on 2 and 212 DF,  p-value: 0.346
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.5544  -0.1430   0.0663   0.2980  16.0468 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)      2.45789    2.00787   1.224    0.222
## trend_2012       1.73076    1.15936   1.493    0.137
## pressures_2012  -0.02623    0.01808  -1.451    0.148
## resilience_2012 -0.02202    0.02104  -1.046    0.297
## 
## Residual standard error: 2.184 on 211 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.01833,    Adjusted R-squared:  0.004377 
## F-statistic: 1.314 on 3 and 211 DF,  p-value: 0.2709

CP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.506   0.665   0.872   1.239  20.996 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.66474    0.54834  -1.212    0.227
## pred_change -0.07333    0.05887  -1.246    0.215
## 
## Residual standard error: 6.556 on 168 degrees of freedom
##   (50 observations deleted due to missingness)
## Multiple R-squared:  0.00915,    Adjusted R-squared:  0.003252 
## F-statistic: 1.551 on 1 and 168 DF,  p-value: 0.2147
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.932   0.376   0.713   1.218  24.687 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.508754   0.935732  -0.544    0.587    
## trend_2012  -9.485762   2.254036  -4.208 4.41e-05 ***
## r_minus_p   -0.009563   0.026235  -0.364    0.716    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.575 on 150 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.108,  Adjusted R-squared:  0.09615 
## F-statistic: 9.085 on 2 and 150 DF,  p-value: 0.0001887
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.560  -0.485   0.634   1.734  26.384 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     10.14953    5.11064   1.986  0.04887 * 
## trend_2012      -7.83222    2.36072  -3.318  0.00114 **
## pressures_2012  -0.13325    0.07217  -1.846  0.06682 . 
## resilience_2012 -0.09371    0.04740  -1.977  0.04991 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.5 on 149 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.1342, Adjusted R-squared:  0.1167 
## F-statistic: 7.697 on 3 and 149 DF,  p-value: 8.139e-05

CS: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## pred_change        0          0      NA       NA
## 
## Residual standard error: 0 on 146 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 146 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## trend_2012         0          0      NA       NA
## r_minus_p          0          0      NA       NA
## 
## Residual standard error: 0 on 122 degrees of freedom
##   (95 observations deleted due to missingness)
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 2 and 122 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0          0      NA       NA
## trend_2012             0          0      NA       NA
## pressures_2012         0          0      NA       NA
## resilience_2012        0          0      NA       NA
## 
## Residual standard error: 0 on 121 degrees of freedom
##   (95 observations deleted due to missingness)
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 3 and 121 DF,  p-value: NA

CW: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4848 -0.3783 -0.0283  0.1962  7.3090 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.030160   0.079765   0.378    0.706
## pred_change -0.010548   0.008351  -1.263    0.208
## 
## Residual standard error: 1.178 on 218 degrees of freedom
## Multiple R-squared:  0.007264,   Adjusted R-squared:  0.002711 
## F-statistic: 1.595 on 1 and 218 DF,  p-value: 0.2079
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5176 -0.3544 -0.0330  0.2651  7.0581 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.101058   0.093086   1.086   0.2788  
## trend_2012   0.153175   0.528110   0.290   0.7721  
## r_minus_p   -0.005819   0.003129  -1.860   0.0643 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.175 on 217 degrees of freedom
## Multiple R-squared:  0.01602,    Adjusted R-squared:  0.006956 
## F-statistic: 1.767 on 2 and 217 DF,  p-value: 0.1733
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4460 -0.3816 -0.0369  0.2376  7.0313 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.662778   0.596572   1.111    0.268  
## trend_2012       0.117102   0.529575   0.221    0.825  
## pressures_2012   0.001485   0.005519   0.269    0.788  
## resilience_2012 -0.009948   0.005344  -1.861    0.064 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.176 on 216 degrees of freedom
## Multiple R-squared:  0.02015,    Adjusted R-squared:  0.006538 
## F-statistic:  1.48 on 3 and 216 DF,  p-value: 0.2208

FP/FIS: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.285  -3.099  -0.290   1.872  39.356 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   1.0398     0.6562   1.585    0.115
## pred_change   0.1397     0.1080   1.294    0.197
## 
## Residual standard error: 7.849 on 218 degrees of freedom
## Multiple R-squared:  0.007617,   Adjusted R-squared:  0.003065 
## F-statistic: 1.673 on 1 and 218 DF,  p-value: 0.1972
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.113  -2.229  -0.591   1.097  38.550 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.41789    0.83929   2.881 0.004363 ** 
## trend_2012  13.98954    3.97192   3.522 0.000522 ***
## r_minus_p   -0.02157    0.02351  -0.917 0.360094    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.655 on 217 degrees of freedom
## Multiple R-squared:  0.06054,    Adjusted R-squared:  0.05188 
## F-statistic: 6.991 on 2 and 217 DF,  p-value: 0.001142
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.311  -2.317  -0.569   1.125  38.293 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.212763   7.281350  -0.029 0.976716    
## trend_2012      14.329206   4.087981   3.505 0.000555 ***
## pressures_2012   0.039772   0.055324   0.719 0.472979    
## resilience_2012  0.007554   0.083457   0.091 0.927965    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.67 on 216 degrees of freedom
## Multiple R-squared:  0.06111,    Adjusted R-squared:  0.04807 
## F-statistic: 4.686 on 3 and 216 DF,  p-value: 0.003419

FP/MAR: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.625  -0.245   1.026   1.043  40.021 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.0359     0.6847  -1.513    0.133    
## pred_change   0.7460     0.1517   4.917 2.75e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.075 on 123 degrees of freedom
##   (95 observations deleted due to missingness)
## Multiple R-squared:  0.1643, Adjusted R-squared:  0.1575 
## F-statistic: 24.18 on 1 and 123 DF,  p-value: 2.75e-06
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.283  -1.703   0.001   1.631  59.025 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  1.00176    1.56065   0.642   0.5222  
## trend_2012   2.20126    1.25171   1.759   0.0812 .
## r_minus_p   -0.01993    0.03889  -0.512   0.6093  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.687 on 121 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.02952,    Adjusted R-squared:  0.01348 
## F-statistic:  1.84 on 2 and 121 DF,  p-value: 0.1632
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.716  -2.063  -0.111   1.755  58.101 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      8.72983    5.24639   1.664   0.0987 .
## trend_2012       2.54574    1.26453   2.013   0.0463 *
## pressures_2012  -0.07680    0.07369  -1.042   0.2994  
## resilience_2012 -0.08913    0.05924  -1.505   0.1351  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.644 on 120 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02459 
## F-statistic: 2.034 on 3 and 120 DF,  p-value: 0.1128

SP/ICO: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.5406  -0.5394  -0.2818   0.5419   4.4507 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.574520   0.231172   2.485   0.0137 *
## pred_change -0.007735   0.026319  -0.294   0.7691  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.636 on 218 degrees of freedom
## Multiple R-squared:  0.000396,   Adjusted R-squared:  -0.004189 
## F-statistic: 0.08637 on 1 and 218 DF,  p-value: 0.7691
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.4913  -0.5583  -0.3083   0.5431   4.5245 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.460498   0.236643   1.946    0.053 .
## trend_2012  0.327503   3.005825   0.109    0.913  
## r_minus_p   0.001393   0.006128   0.227    0.820  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.64 on 217 degrees of freedom
## Multiple R-squared:  0.0003137,  Adjusted R-squared:  -0.0089 
## F-statistic: 0.03405 on 2 and 217 DF,  p-value: 0.9665
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.5547  -0.5951  -0.2968   0.5700   4.5240 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)     -0.625499   1.363389  -0.459    0.647
## trend_2012       0.279833   3.008800   0.093    0.926
## pressures_2012   0.006961   0.012012   0.580    0.563
## resilience_2012  0.012584   0.015135   0.831    0.407
## 
## Residual standard error: 1.642 on 216 degrees of freedom
## Multiple R-squared:  0.003332,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.2407 on 3 and 216 DF,  p-value: 0.8679

SP/LSP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.684 -3.687 -3.646 -3.646 96.354 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  3.64627    1.14447   3.186  0.00165 **
## pred_change  0.02648    0.13589   0.195  0.84566   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.09 on 218 degrees of freedom
## Multiple R-squared:  0.0001742,  Adjusted R-squared:  -0.004412 
## F-statistic: 0.03798 on 1 and 218 DF,  p-value: 0.8457
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.323  -5.469  -2.945  -0.965  94.902 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  1.27205    1.67929   0.757   0.4496  
## trend_2012  -4.14306    2.99058  -1.385   0.1674  
## r_minus_p    0.10928    0.04535   2.409   0.0168 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.89 on 217 degrees of freedom
## Multiple R-squared:  0.0313, Adjusted R-squared:  0.02237 
## F-statistic: 3.506 on 2 and 217 DF,  p-value: 0.03173
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.671  -5.193  -2.980  -0.478  92.485 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     13.03748    7.93271   1.644   0.1017  
## trend_2012      -5.36300    3.08814  -1.737   0.0839 .
## pressures_2012  -0.23553    0.09470  -2.487   0.0136 *
## resilience_2012  0.02003    0.07419   0.270   0.7875  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.84 on 216 degrees of freedom
## Multiple R-squared:  0.04152,    Adjusted R-squared:  0.02821 
## F-statistic: 3.119 on 3 and 216 DF,  p-value: 0.02699

NP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -88.236  -9.195   6.210  14.834  88.797 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.7641     3.2845  -3.582 0.000486 ***
## pred_change  -0.1747     0.2246  -0.778 0.438097    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.5 on 126 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.004779,   Adjusted R-squared:  -0.003119 
## F-statistic: 0.6051 on 1 and 126 DF,  p-value: 0.4381
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -75.785 -10.850   1.616  18.371  90.589 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -8.16424    7.84396  -1.041  0.29996   
## trend_2012  -14.20002    4.24190  -3.348  0.00108 **
## r_minus_p    -0.09716    0.17187  -0.565  0.57287   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.23 on 125 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.08596,    Adjusted R-squared:  0.07134 
## F-statistic: 5.878 on 2 and 125 DF,  p-value: 0.003633
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -69.379 -14.572   1.445  21.109  76.628 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -176.6990    48.8153  -3.620 0.000428 ***
## trend_2012       -14.6324     4.0655  -3.599 0.000460 ***
## pressures_2012     2.0616     0.5858   3.519 0.000606 ***
## resilience_2012    1.4617     0.4755   3.074 0.002600 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.88 on 124 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.1679, Adjusted R-squared:  0.1478 
## F-statistic:  8.34 on 3 and 124 DF,  p-value: 4.277e-05

TR: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.713  -4.751   0.566   3.269  43.557 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -1.6756     0.9277  -1.806  0.07240 . 
## pred_change   0.3445     0.1038   3.320  0.00107 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.17 on 200 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.05223,    Adjusted R-squared:  0.04749 
## F-statistic: 11.02 on 1 and 200 DF,  p-value: 0.00107
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.634  -5.071  -0.001   3.432  41.815 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.08879    1.14068  -0.955  0.34098   
## trend_2012   7.98655    3.02678   2.639  0.00898 **
## r_minus_p    0.02212    0.03443   0.642  0.52146   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.3 on 199 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.03531,    Adjusted R-squared:  0.02561 
## F-statistic: 3.641 on 2 and 199 DF,  p-value: 0.02798
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.673  -5.530  -0.070   3.738  41.609 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     19.23777    8.39340   2.292   0.0230 *
## trend_2012       5.41557    3.16935   1.709   0.0891 .
## pressures_2012  -0.24073    0.09570  -2.515   0.0127 *
## resilience_2012 -0.11101    0.06422  -1.729   0.0854 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.16 on 198 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.06355,    Adjusted R-squared:  0.04936 
## F-statistic: 4.479 on 3 and 198 DF,  p-value: 0.004565